Abstract

Event extraction from texts aims to detect structured information such as what has happened, to whom, where and when. Event extraction and visualization are typically considered as two different tasks. In this paper, we propose a novel approach based on probabilistic modelling to jointly extract and visualize events from tweets where both tasks benefit from each other. We model each event as a joint distribution over named entities, a date, a location and event-related keywords. Moreover, both tweets and event instances are associated with coordinates in the visualization space. The manifold assumption that the intrinsic geometry of tweets is a low-rank, non-linear manifold within the high-dimensional space is incorporated into the learning framework using a regularization. Experimental results show that the proposed approach can effectively deal with both event extraction and visualization and performs remarkably better than both the state-of-the-art event extraction method and a pipeline approach for event extraction and visualization.

Highlights

  • Event extraction, one of the important and challenging tasks in information extraction, aims to detect structured information such as what has happened, to whom, where and when

  • Latent Event Extraction & Visualization (LEEV) improves upon Latent Event Model (LEM) by over 5% in Fmeasure and with regularization, LEEV-R further improves upon LEEV by over 4%

  • Tweets from different events are mixed together and events are evenly distributed across the whole visualization space

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Summary

Introduction

One of the important and challenging tasks in information extraction, aims to detect structured information such as what has happened, to whom, where and when. The outputs of event extraction could be beneficial for downstream applications such as summarization and personalized news systems. Event extraction and visualization are two different tasks and typically studied separately in the literature, these two tasks are highly related. Documents which are close to each other in the low-dimensional visualization space are likely to describe the same event. Jointly learning the two tasks could potentially bring benefits to each other. It is not straightforward to learn event extraction and visualization jointly since event extraction usually relies on semantic parsing results (McClosky et al, 2011) while visualization is accomplished by dimensionality reduction (Iwata et al, 2007; Lopez-Rubio et al, 2002)

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